code: def test_class_labels_shuffle():
      X, y = make_multiplexer_dataset(
          address_bits=2, sample_size=10, random_seed=0, shuffle=True
      )
  
      print(y)
      assert np.array_equal(y, np.array([0, 1, 0, 0, 1, 0, 1, 0, 1, 1]))
      features = np.array(
          [
              [0, 1, 1, 0, 1, 1],
              [0, 1, 1, 1, 1, 0],
              [1, 0, 1, 0, 0, 1],
              [1, 1, 1, 1, 1, 0],
              [0, 1, 0, 1, 1, 0],
              [0, 1, 1, 0, 0, 1],
              [0, 1, 0, 1, 1, 1],
              [0, 1, 0, 0, 0, 0],
              [1, 0, 1, 0, 1, 1],
              [0, 1, 1, 1, 1, 1],
          ]
      )
  
      assert np.array_equal(X, features)
  
file_path: mlxtend/mlxtend/data/tests/test_multiplexer.py
imports: import sys
  import numpy
  from mlxtend.data import make_multiplexer_dataset
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 6 for calling a global function with example usage: test_class_labels_shuffle() without return types